SEA: A Scalable Entity Alignment System

Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). State-of-the-art EA approaches generally use Graph Neural Networks (GNNs) to encode entities. However, most of them train the models and evaluate the results in a fullbatch fashion, which prohibits EA from being scalable on largescale datasets. To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings. SEA can be run on a computer with merely one graphic card. Moreover, SEA encompasses six state-of-the-art EA models and provides access for users to quickly establish and evaluate their own models. Thus, SEA allows users to perform EA without being involved in tedious implementations, such as negative sampling and GPU-accelerated evaluation. With SEA, users can gain a clear view of the model performance. In the demonstration, we show that SEA is user-friendly and is of high scalability even on computers with limited computational resources.Comment: SIGIR'23 Demo Trac

Identification of Conserved and Novel MicroRNAs in Blueberry

MicroRNAs (miRNAs) are a class of small endogenous RNAs that play important regulatory roles in cells by negatively affecting gene expression at both transcriptional and post-transcriptional levels. There have been extensive studies aiming to identify miRNAs and to elucidate their functions in various plant species. In the present study, we employed the high-throughput sequencing technology to profile miRNAs in blueberry fruits. A total of 9,992,446 small RNA tags with sizes ranged from 18 to 30 nt were obtained, indicating that blueberry fruits have a large and diverse small RNA population. Bioinformatic analysis identified 412 conserved miRNAs belonging to 29 families, and 35 predicted novel miRNAs that are likely to be unique to blueberries. Among them, expression profiles of five conserved miRNAs were validated by stem loop qRT-PCR. Furthermore, the potential target genes of conserved and novel miRNAs were predicted and subjected to Gene Ontology (GO) annotation. Enrichment analysis of the GO-represented biological processes and molecular functions revealed that these target genes were potentially involved in a wide range of metabolic pathways and developmental processes. Particularly, anthocyanin biosynthesis has been predicted to be directly or indirectly regulated by diverse miRNA families. This study is the first report on genome-wide miRNA profile analysis in blueberry and it provides a useful resource for further elucidation of the functional roles of miRNAs during fruit development and ripening

Preparation Method of Co 3

Co3O4 nanoparticles were fabricated by a novel, facile, and environment-friendly carbon-assisted method using degreasing cotton. Structural and morphological characterizations were performed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The component of the sample obtained at different temperatures was measured by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). Nitrogen adsorption and desorption isotherms were utilized to reveal the specific surface areas. The formation mechanism of Co3O4 nanoparticles was also proposed, demonstrating that the additive degreasing cotton played an indispensable role in the process of synthesizing the sample. The resultant Co3O4 sample calcined at 600°C exhibited superior electrochemical performance with better specific capacitance and long-term cycling life, due to its high specific surface areas and pores structures. Additionally, it has been proved that this facile synthetic strategy can be extended to produce other metal oxide materials (e.g., Fe3O4). As a consequence, the carbon-assisted method using degreasing cotton accompanied a promising prospect for practical application

AVNP2 protects against cognitive impairments induced by C6 glioma by suppressing tumour associated inflammation in rats

© 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).Glioblastoma is a kind of malignant tumour and originates from the central nervous system. In the last century, some researchers and clinician have noticed that the psychosocial and neurocognitive functioning of patients with malignant gliomas can be impaired. Many clinical studies have demonstrated that part of patients, adults or children, diagnosed with glioblastoma will suffer from cognitive deficiency during their clinical course, especially in long-term survivors. Many nanoparticles (NPs) can inhibit the biological functions of tumours by modulating tumour-associated inflammation, which provokes angiogenesis and tumour growth. As one of the best antiviral nanoparticles (AVNPs), AVNP2 is the 2nd generation of AVNP2 that have been conjugated to graphite-graphene for improving physiochemical performance and reducing toxicity. AVNP2 inactivates viruses, such as the H1N1 and H5N1influenza viruses and even the SARS coronavirus, while it inhibits bacteria, such as MRSA and E. coli. As antimicrobials, nanoparticles are considered to be one of the vectors for the administration of therapeutic compounds. Yet, little is known about their potential functionalities and toxicities to the neurotoxic effects of cancer. Herein, we explored the functionality of AVNP2 on inhibiting C6 in glioma-bearing rats. The novel object-recognition test and open-field test showed that AVNP2 significantly improved the neuro-behaviour affected by C6 glioma. AVNP2 also alleviated the decline of long-term potentiation (LTP) and the decreased density of dendritic spines in the CA1 region induced by C6. Western blot assay and immunofluorescence staining showed that the expressions of synaptic-related proteins (PSD-95 and SYP) were increased, and these findings were in accordance with the results mentioned above. It revealed that the sizes of tumours in C6 glioma-bearing rats were smaller after treatment with AVNP2. The decreased expression of inflammatory factors (IL-1β, IL-6 and TNF-α) by Western blotting assay and ELISA, angiogenesis protein (VEGF) by Western blotting assay and other related proteins (BDNF, NF-ĸB, iNOS and COX-2) by Western blotting assay in peri-tumour tissue indicated that AVNP2 could control tumour-associated inflammation, thus efficiently ameliorating the local inflammatory condition and, to some extent, inhibiting angiogenesis in C6-bearing rats. In conclusion, our results suggested that AVNP2 could have an effect on the peri-tumor environment, obviously restraining the growth progress of gliomas, and eventually improving cognitive levels in C6-bearing rats.Peer reviewedProo

Qwen Technical Report

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.Comment: 59 pages, 5 figure